Detecting usage of copyrighted video content using object recognition

ABSTRACT

Techniques detecting usage of copyrighted video content using object recognition are provided. In one example, a computer-implemented method comprises determining, by a system operatively coupled to a processor, digest information for a video, wherein the digest information comprises objects appearing in the video and respective times at which the objects appear in the video. The method further comprises comparing, by the system, the digest information with reference digest information for reference videos, wherein the reference digest information identifies reference objects appearing in the reference videos and respective reference times at which the reference objects appear in the reference videos. The method further comprises determining, by the system, whether the video comprises content included in one or more of the reference videos based on a degree of similarity between the digest information and reference digest information associated with one or more of the reference videos.

FIELD OF USE

The subject disclosure relates to detecting usage of copyrighted videocontent using object recognition.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products that facilitate detecting usage ofcopyrighted media content are described.

According to an embodiment, a computer-implemented method can comprisedetermining, by a system operatively coupled to a processor, digestinformation for a video, wherein the digest information comprisesobjects appearing in the video and respective times at which the objectsappear in the video. The computer-implemented method can also comprisecomparing, by the system, the digest information with reference digestinformation for reference videos, wherein the reference digestinformation identifies reference objects appearing in the referencevideos and respective reference times at which the reference objectsappear in the reference videos. The computer-implemented method can alsocomprise determining, by the system, whether the video comprises contentincluded in one or more of the reference videos based on a degree ofsimilarity between the digest information and reference digestinformation associated with one or more of the reference videos.

According to another embodiment, another computer-implemented method isprovided. The computer-implemented method can comprise determining, by asystem operatively coupled to a processor, digest information for avideo, wherein the digest information comprises objects appearing in thevideo and an order in which the objects appear in the video. Thecomputer-implemented method can also comprise comparing, by the system,the digest information with reference digest information for referencevideos, wherein the reference digest information identifies referenceobjects appearing in the reference videos and reference orders in whichthe reference objects respectively appear in the reference videos. Thecomputer-implemented method also comprises determining, by the system,whether the video comprises content included in one or more of thereference videos based on a degree of similarity between the digestinformation and reference digest information associated with one or moreof the reference videos.

According to yet another embodiment, a computer program product fordigital video copyright protection is provided. The computer programproduct can comprise a computer readable storage medium having programinstructions embodied therewith. The program instructions can beexecutable by a processing component and cause the processing componentto determine, by the processing component, digest information for avideo, wherein the digest information comprises objects appearing in thevideo, respective times at which the objects appear in the video, andtime lengths between the respective times at which the objects appear inthe video. The program instructions can be further executable by aprocessing component to cause the processing component to compare thedigest information with reference digest information for referencevideos, wherein the reference digest information identifies referenceobjects appearing in the reference videos, respective reference times atwhich the reference objects appear in the reference videos, andreference time lengths between the respective times at which thereference objects appear in the reference videos. Further, the programinstructions can be executable by the processing component and cause theprocessing component to determine whether the video comprises contentincluded in one or more of the reference videos based on a degree ofsimilarity between the digest information and reference digestinformation associated with one or more of the reference videos.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat facilitates detecting usage of copyrighted video content usingobject recognition in accordance with one or more embodiments describedherein.

FIG. 2 illustrates an example video in which objects present inrespective frames of the video have been identified in accordance withone or more embodiments described herein.

FIGS. 3A and 3B provide graphical depictions of example digestinformation for a reference video and a video including content of thereference video in accordance with one or more embodiments describedherein.

FIG. 4 illustrates a chart including example digest information for avideo in accordance with one or more embodiments described herein.

FIG. 5 illustrates another block diagram of an example, non-limitingsystem that facilitates detecting usage of copyrighted video contentusing object recognition in accordance with one or more embodimentsdescribed herein.

FIG. 6 illustrates another example video in which spatial relations ofobjects present in respective frames of the video have been identifiedin accordance with one or more embodiments described herein.

FIG. 7 illustrates another chart including example digest informationfor a video in accordance with one or more embodiments described herein.

FIG. 8 illustrates a block diagram of another example, non-limitingsystem that facilitates detecting usage of copyrighted video contentusing object recognition in accordance with one or more embodimentsdescribed herein.

FIG. 9 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein.

FIG. 10 illustrates a flow diagram of another example, non-limitingcomputer-implemented method that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein.

FIG. 11 illustrates a flow diagram of another example, non-limitingcomputer-implemented method that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein.

FIG. 12 illustrates a flow diagram of another example, non-limitingcomputer-implemented method that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein.

FIG. 13 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

With the rise of media publishing and distribution via the Internet,users around the world can access a voluminous quantity of media content(e.g., video and audio content). Further, the popularity of socialnetworking has made sharing media content a highly profitable mechanismto increase viewership and generate revenue. As a result, new and oldmedia content is regularly downloaded, copied and shared with the touchof a button. Given the ease with which media content can be viewed, andin many scenarios copied or downloaded, the task of detecting theunauthorized usage of copyrighted media content via the Internet hasbecome extremely difficult for humans and even basic computer processingsystems to perform effectively and efficiently. As used herein, a“copyrighted media item” refers to a media item that cannot be copied,reproduced, published, sold or distributed without authorization from anentity or group of entities associated with ownership of the one or morecopyrights for the media item.

The subject disclosure is directed to computer processing systems,computer-implemented methods, apparatus and/or computer program productsthat facilitate efficiently, effectively, and automatically (e.g.,without direct human involvement) detecting usage of copyrighted mediacontent, and more particularly copyrighted video content. For example,usage of copyrighted video content can include the creation of a newvideo by partially modifying an aspect of a copyrighted video (e.g.,changing the coloration, saturation, luminosity, speed). In anotherexample, usage of copyrighted video content can include the creation ofa new video using parts of one or more copyrighted videos. In anotherexample, usage of copyrighted video content can include copying,publishing or selling the copyrighted video content withoutauthorization from the copyright holder.

In order to facilitate detecting unauthorized usage of copyrighted videocontent, one or more embodiments described herein include objectmatching techniques that involve identifying whether a video includescopyrighted video content based on correspondence between visual objectsappearing in the video and the copyrighted video content. In one or moreembodiments, a reference digest database is initially generated usingautomated object recognition for one or more known videos. The one ormore known videos are referred to as “reference videos.” In variousimplementations, the known or reference videos include video contentthat has been copyrighted or otherwise restricted for usage (e.g.,copying, reproducing, publishing, selling, or distributing) by a singleentity or a group of entities. The reference digest database includesinformation identifying the respective reference videos and digestinformation for the respective reference videos related to the objectsappearing in the reference videos. For example, reference digestinformation for each reference video (or, in some embodiments, one ormore reference videos) can include, but is not limited to, informationidentifying objects present in the reference video, detailedcharacteristics of the objects appearing in the reference video (e.g.,object coloration, object type), an order of appearance of the objects,timing of appearance of the objects, and/or spatial relationships of twoor more of the objects included in a same video frame or segment.

Newly identified or received videos can be screened against thereference digest database to determine whether they are or includereference video content. For example, in one or more implementations,digest information can be generated for a new video in the same orsimilar manner employed to generate the reference digest information forthe reference videos. The digest information for the new video isfurther compared against the reference digest information for thereference videos to identify a match between the content of the newvideo and the content of the reference videos based on a degree ofsimilarity between the digest information for the new video and thedigest information for the respective reference videos. If the digestinformation for the new video does not satisfy a defined criterion(e.g., meet a threshold value) relative to reference digest informationfor one or more of the reference videos, the new video and the digestinformation for the new video can be added to the reference digestdatabase as a new entry. However, if the digest information for the newvideo satisfies the defined criterion (has a threshold level ofsimilarity or sameness) one or more policies can be automaticallyenforced that facilitate mitigating the unauthorized usage of thereference video content (e.g., notifying the copyright holder, rejectingor removing publication of the new video, providing a royalty payment tothe copyright holder).

The computer processing systems, computer-implemented methods, apparatusand/or computer program products employ hardware and/or software tosolve problems that are highly technical in nature (e.g., related toautomated detection of correspondence between digital video content andcopyrighted digital video content), that are not abstract and thatcannot be performed as a set of mental acts by a human. For example, ahuman, or even thousands of humans, cannot efficiently, accurately andeffectively manually analyze the voluminous amounts of new electronicvideo content shared via the Internet daily to identify whether thevideo content includes copyrighted video content. One or moreembodiments of the subject computer processing systems, methods,apparatuses and/or computer program products can enable the automateddetection of copyrighted video content in a highly accurate andefficient manner. By employing object recognition and object matching todetect correspondence between video content, the processing time and/oraccuracy associated with the existing digital media copyright detectionsystems is substantially improved. Further, one or more embodiments ofthe subject techniques facilitate automatically and/or accuratelyidentifying videos that have been modified in an attempt to avoidautomated copyright infringement detection. For example, by determiningcorrespondence between timing of appearance of objects in a video and acopyrighted video, one or more embodiments of the object based matchingtechniques described herein facilitate identifying videos that includecopyrighted video content that has been modified. For instance, one ormore embodiments of the subject techniques enable detecting videosincluding copyrighted video content in which the speed of the videocontent has been increased or decreased, one or more new objects havebeen overlaid onto the copyrighted video content, coloration has beenapplied and/or the like.

Further, one or more embodiments of the computer processing systems,computer-implemented methods, apparatuses and/or computer programproducts facilitate automatically mitigating the unauthorized usage ofcopyrighted video content. For example, in response to identifying avideo that is or includes copyrighted video content, one or moreembodiments of the computer processing systems, computer-implementedmethods, apparatuses and/or computer program products can automaticallyreject or remove publication of the video, notify the copyright holder,facilitate provision of a royalty payment to the copyright holder orperform any number of other mitigation functions.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that facilitates detecting usage of copyrighted video content usingobject recognition in accordance with one or more embodiments describedherein. Aspects of systems (e.g., system 100 and the like), apparatusesor processes explained in this disclosure can constitutemachine-executable component(s) embodied within machine(s), e.g.,embodied in one or more computer readable mediums (or media) associatedwith one or more machines. Such component(s), when executed by the oneor more machines, e.g., computer(s), computing device(s), virtualmachine(s), etc. can cause the machine(s) to perform the operationsdescribed.

As shown in FIG. 1, the system 100 can include a server device 102, oneor more networks 124 and one or more digital media sources 126. Theserver device 102 can include digital media management component 104,which can include reception component 106, digest component 108, andmatching component 114. The server device 102 can also include orotherwise be associated with at least one include memory 120 that storescomputer executable components (e.g., computer executable components caninclude, but are not limited to, the digital media management component104 and associated components). The server device 102 can also includeor otherwise be associated with at least one processor 118 that executesthe computer executable components stored in the memory 120. The serverdevice 102 can further include a system bus 116 that can couple thevarious components including, but not limited to, the digital mediamanagement component 104 and associated components, memory 120 and/orprocessor 118. While a server device 102 is shown in FIG. 1, in otherembodiments, any number of different types of devices can be associatedwith or include the components shown in FIG. 1 as part of the digitalmedia management component 104. All such embodiments are envisaged.

The digital media management component 104 facilitates detecting usageof copyrighted media content, and more particularly, detecting usage ofcopyrighted video content. The term “media content” can include, but isnot limited to, streamable and dynamic media (e.g., video, live video,video advertisements, music, music videos, sound files, animations). Theterm “video” includes, but is not limited to, a sequence of images thatform a moving picture. The term “video content” can include, but is notlimited to, two or more consecutive moving images or frames of a video,video segments, animations, single images or frames of a video, andportions of one or more images or frames of a video.

The reception component 106 can receive video content for processing bydigital media management component 104 to detect reference video contentembedded in or associated with another video. For example, receptioncomponent 106 can receive reference videos for processing by digitalmedia management component 104 in association with generating areference digest database 122. The reception component 106 can alsoreceive new videos for processing by digital media management component104 to determine whether the new videos include reference video content.

In various embodiments, the reception component 106 can receive videocontent from one or more digital media sources 126. The one or moredigital media sources 126 can include an internal media source that isaccessible to the server device 102 either directly or via one or morenetworks 124 (e.g., an intranet) or an external media source that isaccessible to the server device 102 via one or more networks (e.g., theInternet). For example, the one or more digital media sources 126 caninclude a computer-readable storage device (e.g., a primary storagedevice, a secondary storage device, a tertiary storage device or anoff-line storage device) that stores video content. In another example,the one or more digital media sources 126 can include an informationstore that provides access to digital media data included in theinformation store via one or more networks 124. In another example, theone or more digital media sources 126 can include a digital mediacontent provider that includes a website or application that providesvideos and other types of content items or services to client devicesvia a network (e.g., the Internet). According to this example, the videocontent provided by the website or application can be downloaded,streamed or merely viewed at a client device.

In some implementations, the reception component 106 can receive a videothat is uploaded to the server device 102 by a client device. Forexample, the media provider can include a media presentation source thathas access to a voluminous quantity (and potentially an inexhaustiblenumber) of shared video files that are uploaded to the media provider byusers of the media provider. The media presentation source can furtherstream these media files to client devices of respective users of themedia provider via the one or more networks 124.

In some implementations, the reception component 106 can scan thedigital media sources 126 for video content, including reference videocontent and/or new video content. For example, the reception component106 can scan various websites, applications, and network accessiblestorage devices for video content that can provide and/or employreference video content in an unauthorized manner (e.g., unauthorizedusage of copyrighted video content). In various embodiments, thereception component 106 can be or include hardware (e.g., a centralprocessing unit (CPU), a transceiver, a decoder), software (e.g., a setof threads, a set of processes, software in execution) or a combinationof hardware and software that facilitates receiving video content fromone or more digital media sources 126.

The various components (e.g., server device 102, digital mediamanagement component 104 and associated components, digital mediasources 126) of system 100 can be connected either directly or via oneor more networks 124. Such networks 124 can include wired and wirelessnetworks, including, but not limited to, a cellular network, a wide areanetwork (WAN) (e.g., the Internet) or a local area network (LAN). Forexample, the server device 102 can communicate with one or more digitalmedia sources 126 (and vice versa) using virtually any desired wired orwireless technology, including, for example, cellular, WAN, wirelessfidelity (Wi-Fi), Wi-Max, WLAN, and etc. Further, although in theembodiment shown the digital media management component 104 is providedon a server device 102, it should be appreciated that the architectureof system 100 is not so limited. For example, the digital mediamanagement component 104 or one or more components of digital mediamanagement component 104 can be located at another device, such asanother server device, a client device, etc.

Digest component 108 can generate digest information for one or morevideos. The term “digest information” is used herein to refer to one ormore visual features associated with a video. Digest information candistinguish the video from one or more other videos and/or identify thevideo, providing functionality similar to that of a fingerprint. Forexample, in the field of audio matching, an acoustic fingerprint is acondensed digital summary of audible features deterministicallygenerated from an audio signal that can be used to identify an audiosample or locate similar items in an audio database. A digest or digestinformation for a video can be likened to an audio fingerprint for anaudio signal wherein the audible features are replaced with visualfeatures included in the video. In one or more implementations, thesevisual features can include, but are not limited to, objects appearingin the video (e.g., persons, places, things). In addition to the objectsappearing in the video, digest information for a video can also include,but is not limited to, detailed characteristics associated with theobjects (e.g., coloration, type), order of appearance of the objects,timing of appearance of the objects and/or spatial relationships betweentwo or more objects appearing in a same frame or segment of the video.In some embodiments, a segment of a video can include any amount of thevideo less than the whole video. For example, a segment of a video caninclude one or more frames of the video (e.g., one frame, two frames,five frames, ten frames) or any selected time segment including one ormore seconds or milliseconds of the video (e.g., one second, twoseconds, five seconds, ten seconds).

In one or more embodiments, digest component 108 can generate acompilation of reference digest information for a set of known referencevideos that have been previously provided or made accessible to thedigital media management component 104 (e.g., via reception component106). For example, the digital media management component 104 can beprovided with a voluminous quantity (and potentially an inexhaustiblenumber) of reference videos and generate digest information for one ormore of (or, in some embodiments, each of) the reference videos. Invarious implementations, the reference videos can include video contentthat has been copyrighted or otherwise restricted for usage (e.g.,copying, reproducing, publishing, selling, or distributing) by a singleentity or group of entities. In some implementations, the digestcomponent 108 can organize the reference digest information for thereference videos in a reference digest database 122. For example, thereference digest database 122 can include information identifying one ormore of (or, in some embodiments, each of) the reference videos and thedigest information respectively associated with the one or more of (or,in some embodiments, each of) the reference videos. The reference digestdatabase 122 can be stored at a location that is accessible to thedigital media management component 104. For example, in the embodimentshown, the reference digest database is provided in the memory 120 ofthe server device 102. However, it should be appreciated that thereference digest database 122 can be located at another suitable devicethat is accessible to the digital media management component 104 via adirect connection or via one or more networks 124.

A digest or digest information for a video can represent the video as awhole and/or can represent slices or segments of the video. For example,a video creator can create a new video by combining pieces or parts oftwo or more reference videos. For instance, the new video can include acompilation of video segments from different reference videos. Thus, inorder to facilitate identifying new videos that include content of areference video, the digest component 108 can generate digestinformation for one or more of the references video that uniquelyidentifies the reference videos as a whole and/or uniquely identifiesone or more different slices or segments of the reference videos. Forexample, the digest component 108 can divide a reference video into twoor more slices or segments, each respectively including parts of thevideo (e.g., one or more portions of the video having a duration lessthan the whole video). Accordingly, as used herein, the term “video” canalso include slices or segments of a whole video.

In some implementations, the digest component 108 can generate digestinformation for slices or segments of the reference videos. In otherimplementations, the digest component 108 can determine whether and/orhow to generate digest information for different slices or segments of areference video based on one or more defined factors. For example, inone embodiment, the digest component 108 can generate reference digestinformation for two or more slices or segments of a reference videobased on duration of the reference video, wherein longer videos aredigested into a greater number of segments than the number of segmentsfor shorter videos. For example, the digest component 108 can employdefined criteria based on the length of the reference video. In someembodiments, reference videos having a duration less than N can bedigested as a whole; reference videos having a duration greater than Nand less than M can be digested as two separate segments; referencevideos having a duration greater than M and less than P are digested asthree separate segments, and so on, wherein N, M, and P are numbersrepresentative of numbers of video frames and/or numbers of seconds orminutes of a reference video. In another embodiment, the digestcomponent 108 can generate reference digest information for differentsegments or slices of the reference videos based on a level ofimportance of the respective reference videos, wherein those referencevideos that are considered more highly ranked than others are digestedinto a greater number of segments or slices. According to this example,the level of rank of a reference video can reflect a cost associatedwith the reference video, such as cost associated with protectionagainst usage of the copyrighted content of the reference video. Forexample, a reference video that is associated with a high royaltypayment in association with usage of content of the reference video maybe classified as having a high importance level and thus digested intoseveral segments or slices to facilitate increasing the probability offinding new videos that include content from only a portion of thereference video. In another embodiment, the digest component 108 cangenerate digest information for different segments or slices ofreference videos based on a quality and/or technical complexity of thevideo (e.g., resolution, compression).

The manner in which the digest component 108 slices a reference video inassociation with generating digest information for the different slicesor segments of the reference video can vary. In some implementations,the digest component 108 can slice a video into segments of equalduration. For example, a reference video that is two minutes long can besliced at the one minute marker into two segments having substantiallyequal length durations. In another implementation, the digest component108 can slice a reference video based on detection or identification ofscene transitions (e.g., using tag metadata that was previouslyassociated with the reference video or another suitable approach),wherein the video is sliced at the respective scene transitions. Inanother example implementation, the digest component 108 can slice areference video based on detection of noteworthy or popular objects,scenes or events in the reference video (e.g., using tag metadata thatwas previously associated with the reference video or another suitableapproach). According to this example, if a reference video includes asegment with an actor reciting a popular quote, the digest component 108can generate first reference digest information for the segment inaddition to generating second reference digest information for the wholereference video.

After the reference digest database 122 has been generated (e.g., bydigest component 108 or another system or entity), the digital mediamanagement component 104 can employ the digest component 108 to generatedigest information for new videos received by or otherwise provided tothe digital media management component 104 (e.g., via receptioncomponent 106) for analysis regarding potential usage of reference videocontent in the new video (e.g., copyright infringement analysis). Forexample, based on reception of a new video, the digest component 108 cangenerate digest information for the new video that includes the same ora similar type of reference digest information as the reference videos.For instance, the digest component 108 can generate digest informationfor the new video that includes but is not limited to, objects appearingin the video, detailed characteristics of the objects (e.g., objectcoloration, object type, etc.), order of appearance of the objects,timing of appearance of the objects and/or spatial relationships betweentwo or more objects appearing in a same frame or segment of the video.The matching component 114 can further compare the digest informationfor the new video with the reference digest information for thereference videos in the reference digest database 122 to determinewhether the new video includes content of one or more of the referencevideos based on a degree of similarity between the digest informationfor the new video and the reference digest information for therespective reference videos.

In one or more embodiments, the digest component 108 can include objectrecognition component 110 and timing component 112 to facilitategenerating digest information for videos (e.g., reference videos, slicesor segments of the reference videos, newly received videos notpreviously included in the reference digest database 122, slices orsegments of the newly received videos). The object recognition component110 can identify objects (e.g., persons, places, things) that appear ina video using hardware, software, or a combination of hardware andsoftware that provides for automated detection of visual objects,appearing in video or digital image. Object recognition algorithms canrely on matching, learning, or pattern recognition algorithms usingappearance-based or feature-based techniques. Techniques include, butare not limited to, edges, gradients, histogram of oriented gradients(HOG), Haar wavelets and/or linear binary patterns.

In various embodiments, the object recognition component 110 canidentify objects appearing in a video using a variety of models,including, but not limited to, extracted features and boosted learningalgorithms, bag-of-words models with features such as speeded-up robustfeatures (SURF) and maximally stable extremal regions (MSER),gradient-based and derivative-based matching approaches, Viola-Jonesalgorithm, template matching, image segmentation and/or blob analysis.

In some implementations, the object recognition component 110 candetermine detailed characteristics associated with identified objects toincrease the accuracy of identifying matching objects included in a newvideo and a reference video. The detailed characteristics can includeany potential characteristic about an object that can be discerned bythe object recognition component 110. For example, the detailedcharacteristics can relate to a coloration of the object, a type of theobject or an identity associated with the object. For instance, inaddition to detecting a car appearing in a video, the object recognitioncomponent 110 can identify a coloration of the car, a make of the car, amodel of the car, a year of the car, a license plate number of the car,etc. In another example, in addition to detecting a person appearing ina video, the object recognition component 110 can determine informationsuch as the identity (e.g., name) of the person, the approximate heightand weight of the person, the clothing worn by the person, etc.

In some implementations, the object recognition component 110 canidentify one or more detectable objects appearing in a video. In otherimplementations, the object recognition component 110 can employdiscriminatory techniques to reduce the amount (and/or associatedprocessing time) of objects identified in a video yet still identifyenough objects to generate a digest for the video that sufficientlydistinguishes the video to facilitate copyright infringement detection.For example, in one embodiment, the object recognition component 110 candetect objects appearing in only a portion of frames or time segments ofthe video (e.g., wherein a time segment of a video can include one ormore frames or seconds of the video less than the whole video).According to this example, the object recognition component 110 canidentify objects appearing in N frames every M frames of the video(e.g., as single frame every ten frames of the video or frames). Inanother example, the object recognition component 110 can select randomframes or time segments of the video to identify and/or characterizeobjects appearing in the video. In another example, the objectrecognition component 110 can select a restricted number (e.g., the topthree) of the most prominent objects identified in each frame or timesegment of the video. Still in another example, the object recognitioncomponent 110 can employ defined criteria regarding types of objects totarget for identification that are considered better candidates forestablishing a unique digest for the video (e.g., particular people withknown identities, known landmarks of physical structures).

Timing component 112 can determine timing information related to timingof appearance of objects identified in a video. For example, the timingcomponent 112 can determine when an object appears in the video (e.g.,referred to herein as the “start time”) and/or when the object no longerappears in the video (e.g., referred to herein as the “stop time”)relative to the beginning and/or the end of the video (or videosegment). Based on a determination of when respective objects appear ina video, the timing component 112 can determine an order of appearanceof objects identified in the video. In addition, based on adetermination of when an object appears and no longer appears, thetiming component 112 can also determine the duration or length ofappearance of the object. The timing component 112 can also determine,in some embodiments, the duration between appearances of differentobject in the video. For example, the timing component 112 can determinethat a white dog appears in a video ten seconds after a blue car appearsin the video. The timing information can be characterized in terms oftime unit (e.g., milliseconds, seconds, minutes, etc.) and/or in termsof frames.

In some embodiments, in association with reception of a video, thedigital media management component 104 can receive metadata associatedwith a video that includes one or more tags identifying previouslyidentified objects appearing in the video and potentially timing ofappearance of the objects. For example, a user uploaded video may bepreviously tagged with information identifying one or more actorsappearing in the video, timing information regarding the appearance ofthe one or more actors, physical structures appearing in the video andassociated timing information, geographical locations depicted in thevideo and associated timing information, etc. According to theseimplementations, the object recognition component 110 and the timingcomponent 112 can respectively employ the metadata previously associatedwith the video to identify and characterize the objects appearing in thevideo and the timing of appearance of the respective objects.

FIG. 2 illustrates an example video 200 in which objects present inrespective frames of the video have been identified (e.g., via objectrecognition component 110 and/or timing component 112 of FIG. 1) inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The video 200 can include a plurality of frames 202 ₁-202 _(n) and has aduration of time T₀-T_(n). It should be appreciated that the number offrames included in a video can vary depending on the duration of thevideo and/or the frame rate of the video. For example, there are threemain frame rate standards in television and digital cinema: 24 framesper second (FPS), 25 (FPS), and 30 (FPS). However, there are manyvariations on these as well as newer emerging standards (e.g., which canvary from 24 FPS to over 100 FPS). For ease of explanation, video 200 isdescribed having a frame rate of 1 FPS. For example, the duration oftime of T₀-T₁ is 1 second, the duration of T₁-T₂ is 1 second, and so on.

In the embodiment shown, objects A, B, C, D, E, and F have respectivelybeen identified in frames 202 ₁-202 ₉ (e.g., via object recognitioncomponent 110 and/or timing component 112). The different letters A, B,C, D, E and F respectively describe different objects. Consecutiveletters are used to indicate the order in which the different objectsappear. For example, object A appears in frames 202 ₁-202 ₃, object Bappears in frames 202 ₂-202 ₃, object C appears in frames 202 ₂-202 ₅,object D appears in frames 202 ₅-202 ₇, object E appears in frames 202₇-202 ₈, and object F appears in frame 202 ₈-202 ₉. Lines -A-, -B-, -C-,-D-, -E-, and -F-, respectively represent the timing and duration ofappearance of objects A, B, C, D, E and F relative to one another invideo 200 over time T₀-T₉. It should be appreciated that the number ofdifferent objects included in respective frames of a video (e.g., video200) can vary. In addition, over the course of a video (e.g., video200), some objects can re-appear. For example, another order of objectsappearing in a video (e.g., video 200) can include A, B, C, A, D, A, E,B, B, E, E, A, etc.

Referring back to FIG. 1, matching component 114 can facilitatedetermining whether a new video received by the digital media managementcomponent 104 (e.g., not previously determined to be a reference videoor associated with the reference digest database 122) is or includesvideo content included in one or more of the reference videos (includingportions or segments/slices of the reference videos). In particular, thematching component 114 can determine whether a new video includescontent included in one or more of the reference videos associated withthe reference digest database 122 based on a degree of similaritybetween the digest information for the new video (e.g., as determined bydigest component 108), and respective reference digest information forthe reference videos. For example, the matching component 114 candetermine that a new video includes content included in one or more ofthe reference videos based on the degree of similarity between thedigest information for the new video and the digest information for theone or more reference videos satisfying a defined matching criterion(e.g., in various embodiments, the similarity level being above orsubstantially equal to a threshold value or percentage).

The matching component 114 can employ various techniques to determine adegree of similarity between digest information for a new video anddigest information for a reference video. These techniques can varybased on the type of digest information associated with the respectivevideos, which can include one or more of objects appearing in the video,characteristics of the objects, order of appearance of the objects,timing of appearance of the objects and/or spatial relationships betweentwo or more objects appearing in a same frame or segment of the video.

The matching component 114 can facilitate automated digital copyrightdetection substantially improving the processing efficiency amongprocessing components in existing digital copyright detection systems,reducing delay in processing performed by the processing componentsand/or improving the accuracy in which the processing systems identifyusage of copyrighted video content. For example, the matching component114 can automatically identify videos that include copyrighted videocontent in a manner that cannot be performed by a human (e.g., usingapproaches that are greater than the capability of a single human mind).For example, the amount of data processed, the speed of processing ofthe data and/or the electronic data types processed by the matchingcomponent 114 over a certain period of time can be respectively greater,faster and different than the amount, speed and data type that can beprocessed by a single human mind over the same period of time. Forexample, video data processed by the matching component 114 can be rawdata (e.g., raw audio data, raw video data, raw textual data, rawnumerical data, etc.) and/or compressed data (e.g., compressed audiodata, compressed video data, compressed textual data, compressednumerical data, etc.) captured by one or more sensors and/or one or morecomputing devices specially designed to obtain and process raw dataand/or compressed data.

In some embodiments, the matching component 114 can determine a matchbetween a new video and a reference video based on a degree insimilarity between the objects appearing in the respective videos beingabove a threshold value. For example, a new video that includes aminimum defined percent of the same objects appearing in a referencevideo can be considered a match with the reference video. For instance,in an example embodiment in which video 200 of FIG. 2 is a referencevideo, the matching component 114 can determine that a new video thatalso includes objects, A, B, C, D, E and F is a match with the referencevideo. The threshold amount can vary (e.g., the minimum defined percentcan be 75%, 85%, 90%, 95%).

The degree of similarity can also account for similarities and/ordifferences between detailed characteristics of the objects. Forexample, the matching component 114 can consider a car appearing in thenew video as not being a match with a car appearing in the referencevideo because the cars are different models (or have differences inother visual features). The degree of scrutiny applied by the matchingcomponent 114 with respect to correspondence between objects can vary.In some embodiments, with respect to coloration of objects, for example,the matching component 114 can account for consistency in colorationdifferences between respective objects appearing in a new video and areference video to determine whether the difference in coloration of therespective objects is attributed to an adaptation of coloration settingsor an effect applied to the new video (e.g., the new video may be ablack and white version of the reference video or the new video may havea blue undertone applied, a different saturation level, a differentluminosity level). According to these embodiments, consistent colorationdifferences between the respective objects of the new video and thereference video would not affect the degree of similarity between theobjects.

In some embodiments, the matching component 114 can also determinewhether a new video includes video content of a reference video based onsimilarity in an order of appearance of the objects appearing in the newvideo and the reference objects appearing in the reference video. Forexample, the matching component 114 can determine a new video includescontent of the reference video based on a determination that the orderof appearance of the objects in the respective videos is the same orsubstantially the same (e.g., with respect to a threshold value orpercentage). For example, in an embodiment in which video 200 of FIG. 2is a reference video, the matching component 114 can determine that anew video that also includes objects, A, B, C, D, E and F (in thatorder) is a match with the reference video. According to thisimplementation, a new video can be a modified version of the referencevideo and can have removed some of the objects included in some framesof the reference video and/or can have added (e.g., as an overlay) newobjects to some frames of the reference video. However, despite thismodification, the remaining common objects of the new video and thereference video will still likely be in the same order. Thus, althoughthe degree of similarity between objects appearing in the new video andobjects appearing in a reference video can be reduced based on editingapplied to the reference video to create the new video, the matchingcomponent 114 can still determine that the new video includes content ofthe reference video based on a combination of the amount of commonobjects between the respective videos and/or the degree of similarity inorder of appearance of the common objects.

In some embodiments, the matching component 114 can further determinewhether a new video includes video content of a reference video based ontiming of appearance of objects in the new video and correspondingobjects appearing in the reference video. This timing of appearanceinformation can include the start times of the respective objects, theend times of the respective objects, the duration of appearance of therespective objects and/or the timing between appearances of differentobjects (also referred to as “difference in start times of objectpairs”).

For example, the matching component 114 can identify a string of objectsincluded in the new video that are also included in a reference video inthe same order. The matching component 114 can further align the starttimes of one of the objects in the string for the new video with thestart time of the corresponding reference object of the string for thereference video (e.g., the first object in the respective strings). Forinstance, in an example embodiment in which video 200 of FIG. 2 is areference video, if the new video and the reference video both includeobjects A, B, C, D, E and F in that order, the matching component 114can align (in time) the start times of object A in both videos and thendetermine whether the start times of objects B, C, D, E and F are alsoaligned in time. If the new video includes content of the referencevideo, the start times of objects A, B, C, D, E and F in both videosshould align if there is a match (e.g., if the new video has not beensped up or slowed down).

In some embodiments, the matching component 114 can also determinewhether the durations of appearance of the respective objects in bothvideos align. For example, in an embodiment in which video 200 of FIG. 2is a reference video, the matching component 114 can determine that anew video that also includes objects, A, B, C, D, E and F match with thereference video if, in addition to including objects A, B, C, D, E and Fin that order, and both videos having aligned start times for objects A,B, C, D, E and F, the duration of appearance of objects A, B, C, D, Eand F in both videos is approximately the same (e.g., if the new videohas not been sped up or slowed down). For instance, if object A appearsin the reference video for three consecutive frames, object A shouldappear in the new video for three consecutive frames if there is amatch. According to this example, the matching component 114 candetermine that a new video includes content of a reference video basedon a degree of similarity in the start times of respective objectsincluded in both videos and/or the degree of similarity in the durationof appearance of the respective objects being above or substantiallyequal to a threshold value.

In another example, the matching component 114 can identify a string ofobjects included in the new video that are also included in a referencevideo in the same order. The matching component 114 can determinewhether differences between the start times of pairs of objects in thestring of the new video correspond with differences between the starttimes of the corresponding pairs of objects in the string of objects inthe reference video.

FIGS. 3A and 3B provide graphical depictions of example digestinformation for a reference video and a video including content of thereference video in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

For example, FIG. 3A provides a graphical depiction of example digestinformation 300 for video 200 in accordance with one or more embodimentsdescribed herein. The digest information 300 for video 200 can includeinformation indicative of an order, timing and/or duration of appearanceof identified objects A, B, C, D, E and F relative to the beginning andthe end of the video, as represented by lines -A-, -B-, -C-, -D-, -E-,and -F- in relation to one another. The digest information 300 for video200 can also include the duration or length of time between the starttimes of different consecutive objects (e.g., identified via the dashedlines). For example, the length between the start times of objects A andB is L₁, the length between the start times of objects B and C is L₂,the length between the start times of objects C and D is L₃, the lengthbetween the start times of objects D and E is L₁, and the length betweenthe start times of objects E and F is L₅.

With reference to FIGS. 1 and 3A, in furtherance to the above examplewherein both the new video and the reference video include objects A, B,C, D, E and F in that order, the matching component 114 can compare thelength of time between the start times of objects A and B, B and C, Cand D, D and E, and E and F for both the new video and the referencevideo (e.g., L₁-L₅). If the length between the start times of therespective pairs of objects is the same (e.g., if the new video has notbeen sped up or slowed down), the matching component 114 can determinethat that new video likely includes content of the reference video.According to this example, the matching component 114 can determine thatthe new video includes content of the reference video based on a degreeof similarity in the differences between the start times of thecorresponding object pairs included in the new video and the referencevideo being above a threshold value.

The matching component 114 can also employ timing of appearanceinformation regarding correspondence between start times of respectiveobjects appearing in a new video and a reference video, correspondencebetween durations of appearances of the respective objects, and/orcorrespondence between difference in start times of corresponding pairsof the objects, to determine when a new video includes content of areference video yet simply has a modified duration relative to thereference video based on speeding up or slowing down of the referencevideo content. For instance, a length of two minutes in between thestart times of objects A and B appearing in the reference video can beconsidered a match against a length of 1 minute and 45 seconds betweenthe start times of objects A and B appearing in the new video if the newcontent video was sped up by 12.5%.

Likewise, if the length of time of appearance of object C in thereference video is 30 seconds, the length of time of appearance ofobject C in the new video would be 33 seconds if the speed of the newvideo was reduced such that the duration of the new video is 10% longerthan the reference video. Accordingly, in some embodiments, the matchingcomponent 114 can determine a relative ratio or percentagerepresentative of the difference in duration between the start times ofa pair of objects appearing in both the new video and the referencevideo, or a relative ratio or percentage representative of thedifference in duration of appearance of an object appearing in both thenew video and the reference video. The matching component 114 canfurther determine that the new video and the reference video match orsubstantially match based on a determination that the start times ofadditional corresponding object pairs in the new video and the referencevideo or the durations of appearance of additional objects in the newvideo and the reference video, consistently differ by the relative ratioor percentage. Thus, the matching component 114 can determine thatcontent of a new video matches that of a reference video if the relativelengths between the start times of object pairs appearing in both videosor the relative durations of appearance of corresponding objects in bothvideos are the same in view of the new video being sped up or sloweddown.

FIG. 3B provides a graphical depiction of example digest information 301for a new video in accordance with one or more embodiments describedherein. As exemplified via comparison of digest information 300 anddigest information 301, the new video can include the same or similarcontent as video 200, yet the new video has a shortened duration (d)relative to video 200 due to an increased speed of the new videorelative to the speed of video 200. For example, similar to video 200,the digest information 301 for the new video includes informationindicative of an order, timing and duration of appearance of identifiedobjects A, B, C, D, E and F relative to the beginning and the end of thevideo, as represented by lines -A-, -B-, -C-, -D-, -E-, and -F- inrelation to one another. The digest information 301 for the new videosubstantially corresponds to the digest information 300 for video 200;however due to an increased speed of the new video relative to video200, the length of time between the start times of different consecutiveobjects A and B, B and C, C and D, D and E and F, L₁′-L₅′, is shorterrelative to L₁-L₅, respectively. However, assuming for example, theratio of L₁′/L₁ is 90%, in some embodiments, the matching component 114can determine that the new video includes content of video 200 if theratios of L₂′/L₂, L₃′/L₃, L₄′/L₄ and L₅′/L₅ are also 90%, respectively(or within a defined range of 90%). It should be appreciated that thelength of time between any two endpoints of lines -A-, -B-, -C-, -D-,-E-, and -F- can also be employed to facilitate determiningcorrespondence between relative timing of appearance of objects in video200 and the new video. For example, the lengths between start times ofobjects A relative to the end times of object D in both videos can becompared to determine whether they differ by a same percentage as thelengths between the end times of object C relative to the end time ofobjects F, and so on.

FIG. 4 illustrates a chart 400 including example digest information forvideo 200 in accordance with one or more embodiments described herein.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

In the embodiment shown, the digest information identifies the objectsthat appear in the video and the order of appearance of the objects overtime T₀-T₉. It should be appreciated that the digest information canspan a longer duration of the video (e.g., T₀-T_(n), wherein n is thelast frame of the video), and that digest information for only a portionof the video is presented merely for exemplary purposes. The digestinformation also includes the start times of appearance of therespective objects, the end times of appearance of the respectiveobjects, the length of appearance of the respective objects (e.g., innumber of frames or seconds), and the lengths (e.g., L₁-L₅) between therespective start times of different consecutive objects (e.g., in numberof frames or seconds).

FIG. 5 illustrates another block diagram of an example, non-limitingsystem 500 that facilitates detecting usage of copyrighted video contentusing object recognition in accordance with one or more embodimentsdescribed herein. System 500 includes same or similar features andfunctionalities as system 100 with the addition of spatial recognitioncomponent 502 and filter component 504 to digital media managementcomponent 104. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

In various embodiments, the digest component 108 can include spatialrecognition component 502 to determine spatial relationships betweendifferent objects appearing in a same frame or segment of a video (e.g.,wherein a segment can include a plurality of consecutive frames). Insome implementations, the spatial recognition component 502 can identifyrelative sizes of objects appearing in the same frame or segment of avideo. For example, a segment of a surfing video can include a surfer inthe forefront of the frame riding a wave on a red surfboard with ayellow boat in the distance. According to this example, the spatialrecognition component 502 can determine the size of the red surfboardrelative to the size of the yellow boat. In another implementation, thespatial recognition component 502 can determine relative positions oftwo or more objects appearing in the same frame or segment of a videorelative to a two-dimensional or three-dimensional coordinate space. Forexample, with respect to a rectangular frame and a two dimensionalcoordinate space wherein the bottom region of the frame corresponds toan x-axis and the left side of the frame corresponds to a y-axis, thespatial recognition component 502 can determine coordinatescorresponding to the positions of the objects appearing in the framerelative to the coordinate spaces. In some embodiments, the spatialrecognition component 502 can further determine distances between two ormore objects appearing in the same frame or video segment of a video.

For example, FIG. 6 illustrates another example of video 200 withobjects present and in which spatial relationships of objects present inrespective frames of the video have been identified (e.g., via spatialrecognition component 502) in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

In particular, in addition to identifying objects A, B, C, D, E and Frespectively appearing in frames 202 ₁-202 ₉, the objects arerespectively depicted in circles having a determined size (e.g.,diameter) and location in the frame. For example, frame 202 ₃ includesthree circles for objects A, B and C respectively having different sizesand locations in the frame.

FIG. 7 illustrates another chart 700 including example digestinformation for video 200 in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

In the embodiment shown, the digest information identifies the objectsthat appear in respective frames 202 ₁-202 ₉ of the video. The digestinformation also includes coordinates of the respective objects in theframes relative to a two-dimensional coordinate space, wherein therespective frames have a rectangular shape and the x-axis corresponds tothe bottom region of the respective frames and the y-axis corresponds tothe left side of the respective frames. For those frames including twoor more objects (e.g., frames 202 ₁, 202 ₃, 202 ₅, 202 ₇, and 202 ₈),the relative size ratio of the objects to one another can be alsoidentified.

In some embodiments, information regarding spatial relationships betweentwo or more objects appearing in a same frame or segment of a video canbe added to the digest information for the video to facilitateidentifying usage of reference video content in another video. Forexample, matching component 114 can determine whether a new videoincludes content of a reference video based on both videos including twoor more of the same objects appearing in a same frame or segment andhaving a same or relatively the same spatial relationship. Because thespatial relationships between two objects appearing in a same frame orsegment of a video do not change if the dimensions of the video frame orsegment are modified, digest information regarding spatial relationshipsbetween two or more objects appearing in a same frame or segment of avideo can facilitate identifying new videos that include reference videocontent in a manner that modifies the frame size of the reference videocontent. For example, a new video can include the surfing video in theexample above playing on a television in the background while a group ofsurf instructors provide commentary on the surfing video. According tothis example, although the content of the surfing video does not make upthe entirety of the frame dimensions, the relative sizes and positionsof the red surfboard relative to the yellow boat will be the same (orapproximately the same), thus facilitating detection of the usage of thesurfing video content in the new video.

Referring back to FIG. 5, in various embodiments, the matching component114 can employ one or more analysis techniques that account for thedegree of similarity between digest information for a new video and areference video with respect to a combination of the various types ofdigest information described herein, including, but not limited to,objects appearing in the video, detailed characteristics associated withthe objects (e.g., object coloration, object type, etc.), order ofappearance of the objects, timing of appearance of the objects (whichincludes relative timing to account for new videos that have been spedup or slowed down) and/or spatial relationships between two or moreobjects appearing in a same frame or segment of the video.

In some implementations, the matching component 114 can determine amatch score that accounts for a total degree of similarity between a newvideo and a reference video with respect to a combination of one or moreof the factors previously noted. For example, a new video can have a 75%degree of similarity with respect to shared objects, a 90% degree ofsimilarity with respect to order of appearance of the shared objects, a95% degree of similarity with respect to timing of appearance of theshared objects, and 100% degree of similarity with respect to spatialrelationship between two or more of the shared objects appearing in thesame frame or segment of the video, resulting in a match score of 90%degree of similarity.

In some embodiments, the matching component 114 can also apply differentweights to the respective factors that are determined to have a higherimpact on matching accuracy (e.g., timing of appearance of objectsversus number of corresponding objects). The matching component 114 canfurther employ a threshold requirement wherein new videos having a matchscore greater than a defined value can be considered a match with thereference video.

In some embodiments, the matching component 114 can employ filtercomponent 504 to facilitate enhancing the efficiency with which thematching component 114 determines whether a new video includes referencevideo content (e.g., using processor 118 or another processor). Forexample, rather than scanning a new video against every entry in thereference digest database 122 with respect to each type of digestinformation category, the matching component 114 can employ the filtercomponent 504 to reduce the number of potential candidate referencevideos to evaluate more thoroughly. For example, the filter component504 can apply one or more first filtering criteria that reduce the setof reference video in the reference digest database 122 to a smallersubset of potential matches. The matching component 114 can furtherevaluate the subset of the reference videos with respect to one or moresecond criteria. In an example, the filter component 504 can apply ahierarchy filtering scheme, wherein the filter component 504 firstidentifies a first subset of the reference videos having at least afirst defined percent of shared objects with a new video. The filtercomponent 504 can then reduce the first subset of the reference videosto second subset of reference videos based on a degree of correspondencebetween an order of appearance of the shared objects being anotherthreshold value. In some embodiments, the matching component 114 canfurther evaluate the second subset of reference videos based oncorrespondence between timing of appearance of the respective objectsand/or degree of similarity between spatial relationships of two or moreof the shared objects appearing in a same frame or segment of the newvideo and the reference videos.

In another example embodiment, the matching component 114 can evaluate avideo for potential usage of reference video content by employing thefilter component 504 to apply a first filter that identifies a subset ofthe reference videos in the reference digest database 122 having atleast a defined number of corresponding objects to an evaluated videoand appearing in a same order as the evaluated video. According to thisexample, the subset of the reference videos can include reference videosthat have different amounts of corresponding objects depending on theamount of reference video content included in the evaluated video. Forexample, the evaluated video can include a short segment of a longreference video and thus include only a small subset of objectsappearing in the reference video. In another example, the evaluatedvideo can include objects removed from the reference video content. Inyet another example, the evaluated video can include added objectsoverlaid on reference video content.

In some embodiments, the matching component 114 can further isolate onlythe matching objects between the video and the respective referencevideos in the subset and determine whether other factors including, forexample, timing of appearance of the matching objects, specific detailsof the matching objects or spatial relationships of two or more matchingobjects appearing in a same video frame or segment, respectivelycorrespond (e.g., either exactly or relatively in consideration of amodified timing of the video, a coloration effect applied to the video,or a modified frame size of the video).

FIG. 8 illustrates a block diagram of another example, non-limitingsystem 800 that facilitates detecting usage of copyrighted video contentusing object recognition in accordance with one or more embodimentsdescribed herein. System 800 includes the same or similar aspects assystem 500 with the addition of publication component 802, notificationcomponent 804 and compensation component 806 to digital media managementcomponent 104. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

In various embodiments, in response to a determination that a new videodoes not include media content included in reference video, the digestcomponent 108 can add information identifying the new video and digestinformation for the new video to the reference digest database 122. Thenew video can thus become a new reference video in some embodiments. Insome implementations, the notification component 804 can generate and/ortransmit a notification (e.g., using an electronic message or othernotification approach) to an entity affiliated with the new video (e.g.,an entity affiliated with the new video can be a human or electronicuploader of the new video). The notification can inform the entity thatthe new video does not include reference video content (e.g.,copyrighted).

In an embodiment in which the server device 102 is or is affiliated witha media content provider (e.g., a video sharing and streaming service, asocial networking system that provides for sharing or video content),the digital media management component 104 can process a new video withrespect to detecting usage of reference media content in associationwith a request to publish the new video via the media content provider.According to this embodiment, in response to a determination that thenew video does not include reference video content, the publicationcomponent 802 can proceed to publish the new video on a networkaccessible platform (e.g., a website, an application, etc.) employed bythe media provider to distribute the media content.

The digital media management component 104 can further facilitatemitigating the unauthorized use of reference (e.g., copyrighted) videocontent. For example, in an embodiment in which a video includingreference video content is associated with a request to publish thevideo, the publication component 802 can automatically rejectpublication of the video on the network accessible platform employed bythe media provider and/or, in some embodiments, the notificationcomponent 804 can generate a notification that informs an entityassociated with use of the video (e.g., an uploader of the new video)that the video is or includes reference video content. In someimplementations, the notification can identify the reference videocontent and indicate a match score determined by the matching component114 regarding the degree of similarity between the video content and thereference video content.

In another embodiment, in response to a determination by matchingcomponent 114 that a video is or includes reference video content, thenotification component 804 can generate and/or transmit a notificationto an entity associated with ownership of the reference video content(e.g., the copyright holder, a licensee of the copyright holder). Thenotification can inform the entity regarding usage of the referencevideo content, identify the video that is or includes the referencevideo content, and/or identify the entity associated with creation orusage of the video. In some implementations, the notification caninclude contact information for the entity associated with usage and/orcreation of the video.

In various additional embodiments, compensation component 806 canfacilitate providing entities associated with ownership of referencevideo content (e.g., the copyright holders, licensees of the copyrightholders) compensation for usage of their reference video content byothers. For example, in one implementation, in response to adetermination (e.g., by matching component 114) that a particular videois or includes reference video content, the compensation component 806can facilitate provision of a royalty payment to the entity that ownsthe reference video content. For instance, the compensation component806 can charge the user associated with upload of the particular video adefined royal payment that accounts for the manner in which the useremployed or is intended to employ the reference video content.

While FIGS. 1, 5 and 7 depict separate components in the digital mediamanagement component 104, it is to be appreciated that two or morecomponents can be implemented in a common component. Further, it is tobe appreciated that the design of the digital media management component104 can include other component selections, component placements, etc.,to facilitate identifying usage of reference (e.g., copyrighted) videocontent and mitigating the unauthorized usage of the reference videocontent. Moreover, the aforementioned systems and/or devices have beendescribed with respect to interaction between several components. Itshould be appreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentscan be combined into a single component providing aggregatefunctionality. The components can also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

Further, some of the processes performed may be performed by specializedcomputers for carrying out defined tasks related to the digital mediacopyright detection subject area. The subject computer processingsystems, methods apparatuses and/or computer program products can beemployed to solve new problems that arise through advancements intechnology, computer networks, the Internet and the like. The subjectcomputer processing systems, methods apparatuses and/or computer programproducts can provide technical improvements to automated digitalcopyright detection systems by improving processing efficiency amongprocessing components in digital copyright detection systems, reducingdelay in processing performed by the processing components, andimproving the accuracy in which the processing systems identify usage ofcopyrighted video content.

FIG. 9 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 900 that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein. At 902, a system operativelycoupled to a processor determines digest information for a video,wherein the digest information comprises objects appearing in the videoand respective times at which the objects appear in the video (e.g., viadigest component 108). At 904, the system compares the digestinformation with reference digest information for reference videos,wherein the reference digest information identifies reference objectsappearing in the reference videos and respective reference times atwhich the reference objects appear in the reference videos (e.g., viamatching component 114). At 906, the system determines whether the videocomprises content included in one or more of the reference videos basedon a degree of similarity between the digest information and referencedigest information associated with one or more of the reference videos(e.g., via matching component 114). For example, the system candetermine that the video includes reference video content based ondigest information for the video and the reference video indicating thatthe respective videos include a substantial amount (e.g., with respectto a threshold value) of common objects, the common objects appear inboth videos in a substantially same order (e.g., with respect to athreshold percentage) and/or the timing of appearance of the commonobject in both videos is substantially the same (e.g., with respect to athreshold value or range of time values).

FIG. 10 illustrates a flow diagram of another example, non-limitingcomputer-implemented method 1000 that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein. At 1002, a system operativelycoupled to a processor determines digest information for a video,wherein the digest information comprises objects appearing in the videoand respective times at which the objects appear in the video (e.g., viadigest component 108). At 1004, the system compares the digestinformation with reference digest information for reference videos,wherein the reference digest information identifies reference objectsappearing in the reference videos and respective reference times atwhich the reference objects appear in the reference videos (e.g., viamatching component 114). At 1006, the system determines that the videocomprises first content included in a first reference video of thereference videos based on a degree of similarity between the digestinformation and first reference digest information of the referencedigest information for the first reference video being greater than athreshold degree of similarity (e.g., via matching component 114). At1008, the system mitigates unauthorized usage of the first based on thedetermining that the video comprises the first content included in thefirst reference video (e.g., via publication component 802, notificationcomponent 804 or compensation component 806). For example, the systemcan reject a request to publish the video on a network. In anotherexample, the system can notify an entity associated with ownership ofthe first reference video regarding the video, the usage of thereference video content in the video, the degree of similarity betweenthe video content and the reference video content, and/or contactinformation for the user and/or creator of the video. In anotherexample, the system can facilitate providing the entity associated withownership of the reference video a royalty payment by the user of thevideo.

FIG. 11 illustrates a flow diagram of another example, non-limitingcomputer-implemented method 1100 that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein. At 1102, a system operativelycoupled to a processor determines digest information for a video,wherein the digest information comprises objects appearing in the videoand respective times at which the objects appear in the video (e.g., viadigest component 108). At 1104, the system compares the digestinformation with reference digest information for reference videos,wherein the reference digest information identifies reference objectsappearing in the reference videos and respective reference times atwhich the reference objects appear in the reference videos (e.g., viamatching component 114). At 1106, based on the comparing, the systemidentifies a first reference video of the reference videos comprising atleast some first reference objects that correspond to the objects in thevideo and that occur at first respective reference times in thereference video similar to the times at which the objects appear in thevideo (e.g., via matching component 114). At 1108, the system identifiesa base time length separating two of the objects appearing in the videoat different times. For example, with reference to FIGS. 3A and 3B,assuming digest information 301 represents digest information for thevideo of method 1100 and digest information 300 represents digestinformation for the first reference video of method 1100, the system canidentify any length between two end points of lines -A-, -B-, -C-, -D-,-E-, and -F- as the base length, such as L₁′. At 1110, the systemidentifies a reference time length separating two first referenceobjects of the at least some first reference objects corresponding tothe two of the objects appearing in the video. For example, withreference to FIGS. 3A and 3B, if the system selects L₁′ as the base timelength, the system will select L₁ as the reference time length. At 1112,the system can further employ a ratio of the base time length relativeto the reference time length to determine whether the video comprisesfirst content included in the first reference video of the referencevideos. For example, the system can employ the ratio of L₁′/L₁ todetermine whether additional lengths between other objects appearing inthe video (e.g., L₂′, L₃′, L₄′, and L₅′) and corresponding lengthsbetween other reference objects in the reference video (e.g., L₂, L₃,L₄, and L₅) respectively and consistently differ by the ratio.Accordingly, the system can identify (e.g., via matching component 114),new videos that include sped up or slowed down versions of referencevideo content.

FIG. 12 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 1200 that facilitates detecting usage ofcopyrighted video content using object recognition in accordance withone or more embodiments described herein. At 1202, a system operativelycoupled to a processor, determines digest information for a video,wherein the digest information comprises objects appearing in the videoand an order in which the respective objects appear in the video (e.g.,via digest component 108). At 1204, the system compares the digestinformation with reference digest information for reference videos,wherein the reference digest information identifies reference objectsappearing in the reference videos and reference orders in which therespective reference objects respectively appear in the reference videos(e.g., via matching component 114). At 1206, the system determineswhether the video comprises content included in one or more of thereference videos based on a degree of similarity between the digestinformation and reference digest information associated with one or moreof the reference videos (e.g., via matching component 114).

For simplicity of explanation, the computer-implemented methodologiesare depicted and described as a series of acts. It is to be understoodand appreciated that the subject innovation is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts can berequired to implement the computer-implemented methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the computer-implementedmethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be further appreciated that the computer-implementedmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such computer-implemented methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer-readabledevice or storage media.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 13 as well as the following discussion are intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. FIG.13 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. Withreference to FIG. 13, a suitable operating environment 1301 forimplementing various aspects of this disclosure can also include acomputer 1312. The computer 1312 can also include a processing unit1314, a system memory 1316, and a system bus 1318. The system bus 1318couples system components including, but not limited to, the systemmemory 1316 to the processing unit 1314. The processing unit 1314 can beany of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit1314. The system bus 1318 can be any of several types of busstructure(s) including the memory bus or memory controller, a peripheralbus or external bus, and/or a local bus using any variety of availablebus architectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI). The system memory 1316 can alsoinclude volatile memory 1320 and nonvolatile memory 1322. The basicinput/output system (BIOS), containing the basic routines to transferinformation between elements within the computer 1312, such as duringstart-up, is stored in nonvolatile memory 1322. By way of illustration,and not limitation, nonvolatile memory 1322 can include read only memory(ROM), programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 1320 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 1312 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 13 illustrates, forexample, a disk storage 1324. Disk storage 1324 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 1324 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1324 to the system bus 1318, a removableor non-removable interface is typically used, such as interface 1326.FIG. 13 also depicts software that acts as an intermediary between usersand the basic computer resources described in the suitable operatingenvironment 1301. Such software can also include, for example, anoperating system 1328. Operating system 1328, which can be stored ondisk storage 1324, acts to control and allocate resources of thecomputer 1312. System applications 1330 take advantage of the managementof resources by operating system 1328 through program modules 1332 andprogram data 1334, e.g., stored either in system memory 1316 or on diskstorage 1324. It is to be appreciated that this disclosure can beimplemented with various operating systems or combinations of operatingsystems. A user enters commands or information into the computer 1312through input device(s) 1336. Input devices 1336 include, but are notlimited to, a pointing device such as a mouse, trackball, stylus, touchpad, keyboard, microphone, joystick, game pad, satellite dish, scanner,TV tuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1314through the system bus 1318 via interface port(s) 1338. Interfaceport(s) 1338 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1340 usesome of the same type of ports as input device(s) 1336. Thus, forexample, a USB port can be used to provide input to computer 1312, andto output information from computer 1312 to an output device 1340.Output adapter 1342 is provided to illustrate that there are some outputdevices 1340 like monitors, speakers, and printers, among other outputdevices 1340, which require special adapters. The output adapters 1342include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1340and the system bus 1318. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1344.

Computer 1312 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1344. The remote computer(s) 1344 can be a computer, a server, a router,a network PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 1312.For purposes of brevity, only a memory storage device 1346 isillustrated with remote computer(s) 1344. Remote computer(s) 1344 islogically connected to computer 1312 through a network interface 1348and then physically connected via communication connection 1350. Networkinterface 1348 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL). Communicationconnection(s) 1350 refers to the hardware/software employed to connectthe network interface 1348 to the system bus 1318. While communicationconnection 1350 is shown for illustrative clarity inside computer 1312,it can also be external to computer 1312. The hardware/software forconnection to the network interface 1348 can also include, for exemplarypurposes only, internal and external technologies such as, modemsincluding regular telephone grade modems, cable modems and DSL modems,ISDN adapters, and Ethernet cards.

Embodiments of the present invention may be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention. The computer readable storage mediumcan be a tangible device that can retain and store instructions for useby an instruction execution device. The computer readable storage mediumcan be, for example, but is not limited to, an electronic storagedevice, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of various aspects of thepresent invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to customize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method, comprising:determining, by a system operatively coupled to a processor, digestinformation for a video, wherein the digest information comprisesobjects appearing in the video and respective times at which the objectsappear in the video; identifying, by the system, a base time lengthseparating two of the objects appearing in the video at different times;and employing, by the system, a ratio of the base time length relativeto a reference time length separating two first reference objectscorresponding to two of the objects to determine whether the videocomprises first content included in the first reference video of thereference videos.
 2. The computer-implemented method of claim 1, whereinthe determining comprises: identifying, by the system, a first referencevideo of the reference videos comprising at least some first referenceobjects that correspond to the objects in the video and that occur atfirst respective reference times in the first reference videocorresponding to times at which the objects appear in the video, therebyfacilitating improved processing time for automatic detection ofunauthorized usage of reference video content.
 3. Thecomputer-implemented method of claim 1, wherein the determining thedigest information for the video further comprises determining timelengths between the respective times at which the objects appear in thevideo, and wherein the reference digest information further comprisesreference time lengths between the respective reference times at whichthe reference objects appear in the reference videos.
 4. Thecomputer-implemented method of claim 1, further comprising: determining,by the system, that the video comprises first content included in afirst reference video of the reference videos based on a first degree ofsimilarity between the digest information and first reference digestinformation of the reference digest information for the first referencevideo being greater than a threshold degree of similarity, and whereinthe determining that the video comprises the first content included inthe first reference video facilitates mitigating, by the system,unauthorized usage of the first content.
 5. The computer-implementedmethod of claim 1, further comprising: determining, by the system, thatthe video comprises first content included in a first reference video ofthe reference videos based on a first degree of similarity between thedigest information and first reference digest information of thereference digest information for the first reference video satisfying acondition related to a threshold degree of similarity; and rejecting, bythe system, publication of the video on a network based on thedetermining that the video comprises the first content included in thefirst reference video.
 6. The computer-implemented method of claim 1,further comprising: determining, by the system, that the video comprisesfirst content included in a first reference video of the referencevideos based on a first degree of similarity between the digestinformation and first reference digest information of the referencedigest information for the first reference video satisfying a criterionrelated to a threshold degree of similarity; and generating, by thesystem, a notification identifying the first content included in thevideo, the first reference video and the degree of similarity; andtransmitting, by the system, the notification to an entity associatedwith ownership of the first reference video.
 7. The computer-implementedmethod of claim 6, further comprising: facilitating, by the system,provision of a royalty payment to the entity based on the determiningthat the video comprises the first content included in the firstreference video.
 8. The computer-implemented method of claim 1, whereinthe determining comprises: identifying, by the system, a first referencevideo of the reference videos comprising at least some first referenceobjects that correspond to the objects in the video and that occur atfirst respective reference times in the first reference video similar tothe times at which the objects appear in the video.
 9. Thecomputer-implemented method of claim 1, wherein the determiningcomprises: determining that the video comprises first content includedin a first reference video of the reference videos based on firstreference digest information for the first reference video indicatingfirst reference objects in the first reference video substantiallycorrespond to the objects in the video relative to a threshold degree ofcorrespondence, and the first reference objects and the objectsrespectively appear in the first reference video and the video in a sameorder.
 10. A computer-implemented method, comprising: determining, by asystem operatively coupled to a processor, digest information for avideo, wherein the digest information comprises objects appearing in thevideo, an order in which the objects appear in the video, and respectivetimes at which the objects appear in the video; identifying, by thesystem, a base time length separating two of the objects appearing inthe video at different times; and employing, by the system, a ratio ofthe base time length relative to a reference time length separating twofirst reference objects corresponding to two of the objects to determinewhether the video comprises first content included in the firstreference video of the reference videos.
 11. The computer-implementedmethod of claim 10, wherein the determining comprises: identifying, bythe system, a first reference video of the reference videos comprisingat least some reference objects that correspond to the objects in thevideo and that occur in a first reference order corresponding to theorder in which the objects appear in the video, thereby facilitatingimproved processing efficiency for the processor.
 12. Thecomputer-implemented method of claim 10, wherein the determining thedigest information for the video further comprises determining timelengths between the respective times at which the objects appear in thevideo, and wherein the reference digest information further comprisesreference time lengths between respective reference times at which thereference objects appear in the reference videos.
 13. Thecomputer-implemented method of claim 10, further comprising:determining, by the system, that the video comprises first contentincluded in a first reference video of the reference videos based on afirst degree of similarity between the digest information and firstreference digest information of the reference digest information for thefirst reference video being greater than a threshold degree ofsimilarity, and wherein the determining that the video comprises thefirst content included in the first reference video facilitatesmitigating, by the system, unauthorized usage of the first content. 14.The computer-implemented method of claim 10, wherein the determiningcomprises: identifying, by the system, a first reference video of thereference videos comprising at least some first reference objects thatcorrespond to the objects in the video and that occur in a firstreference order in the first reference video corresponding to the orderin which the objects appear in the video; and identifying, by thesystem, a reference time length separating two first reference objectsof the at least some first reference objects corresponding to the two ofthe objects appearing in the video.
 15. The computer-implemented methodof claim 10, wherein the determining the digest information for thevideo further comprises determining spatial relationships betweensubsets of the objects appearing in same frames of the video, andwherein the reference digest information further comprises referencespatial relationships between reference subsets of the reference objectsrespectively appearing in same reference frames of the reference videos.16. The computer-implemented method of claim 10, wherein the determiningcomprises: determining that the video comprises first content includedin a first reference video of the reference videos based on firstreference digest information for the first reference video indicatingfirst reference objects in the first reference video correspond to theobjects in the video, and the first reference objects and the objectsrespectively appear in the first reference video and the video in a sameorder.
 17. A computer program product for digital video copyrightprotection, the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a processing component to cause theprocessing component to: determine, by the processing component, digestinformation for a video, wherein the digest information comprisesobjects appearing in the video, respective times at which the objectsappear in the video, and time lengths between the respective times atwhich the objects appear in the video; identifying, by the system, abase time length separating two of the objects appearing in the video atdifferent times; and employing, by the system, a ratio of the base timelength relative to a reference time length separating two firstreference objects corresponding to two of the objects to determinewhether the video comprises first content included in the firstreference video of the reference videos.
 18. The non-transitory computerprogram product of claim 17, wherein the program instructions arefurther executable by the processing component to cause the processingcomponent to: identify, by the processing component, a first referencevideo of the reference videos comprising at least some first referenceobjects corresponding to the objects in the video, the at least somefirst reference objects occurring at first respective reference times inthe first reference video corresponding to the times at which theobjects appear in the video, and first reference time lengths betweenthe first respective reference times corresponding to the time lengthsbetween the respective times at which the objects appear in the video.19. The non-transitory computer program product of claim 17, wherein theprogram instructions are further executable by the processing componentto cause the processing component to: determine, by the processingcomponent, that the video comprises first content included in a firstreference video of the reference videos based on a first degree ofsimilarity between the digest information and first reference digestinformation of the reference digest information for the first referencevideo satisfying a defined criterion associated with a threshold degreeof similarity; and mitigate, by the processing component, unauthorizedusage of the content based on determining that the video comprises thefirst content included in the first reference video.
 20. Thenon-transitory computer program product of claim 17, wherein the digestinformation for the video further comprises spatial relationshipsbetween subsets of the objects appearing in same frames of the video asdetermined by the processing component, and wherein the reference digestinformation further comprises reference spatial relationships betweenreference subsets of the reference objects respectively appearing insame reference frames of the reference videos.